Multiattribute acceptance sampling plans - Library(ISI Kolkata ...

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‘Print Output as worksheet Worksheets(”Sheet1”).Cells (i , 1).Value = a1 Worksheets (”Sheet1”).Cells (i, 2).Value = cost i = i + 1 Next a1 End Sub ‘Computation of the value of the gamma Poisson for a given set of parameters using the Excel worksheet function Function gammapoisson (x, npbar, s) theta = s / (s + npbar) Gammanum = Exp(-1) / Application.WorksheetFunction.GammaDist(1, s + x, 1, False) onedeno = Exp(-1) / Application.WorksheetFunction.GammaDist(1, s, 1, False) Twodeno = Exp(-1) / Application.WorksheetFunction.GammaDist(1, x + 1, 1, False) gammapoisson = (Gammanum / (onedeno * Twodeno)) * (theta ŝ) * ((1 - theta) ˆx) End Function 139
References Ailor, R. B., Schmidt, J. W. and Bennett, G. K. (1975). The design of economic acceptance sampling Plans for a mixture of attributes and variables. AIIE Transactions, 7. Anscombe, F. J. (1951). The cost of inspection. “Statistical Methods in Industrial Production”, Royal Statistical Society, London. Barnard, G. A. (1946). Sequential tests in industrial statistics. J. Roy. Statist. Soc., B, 8, 1- 26. Barnard, G. A. (1954). Sampling inspection and statistical decisions. J. Roy. Statist. Soc. B, 16, 151-165. Bowker, A. H. and Lieberman, G. L.(1955). “Hand Book of Industrial Statistics”, Englewood Cliffs, N. J, Prentince Hill Inc. Bray, D. F., Lyon, D.A and Burr, I. W. (1973). Three class attributes plans in acceptance sampling. Technometrics, 15, 575-585. Case, K. E., Schmidt, J. W. and Bennett, G. K. (1972). Cost-based acceptance sampling. Industrial Engineering, 4, (11), 26-31. Case, K. E., Schmidt, J.W. and Bennett, G. K. (1975). A discrete economic multiattribute acceptance sampling. AIIE Trans., 363-369. Champernowne, D. G. (1953). The economics of sequential sampling procedures for defectives. Applied Statist., 2, 118-130. Chiu, W. K.(1974). A new prior distribution for attributes sampling. Technometrics, 16, 93-102. Deming, W. E. (1950) . “Some Theory of Sampling”, John Willey and Sons, New York. Dodge, H. F. (1948). Administration of sampling inspection plan. Industr. Qual. Contr., 12-19; reprinted in J. Qual. Tech., 9, 131-138 (1977). Dodge, H. F. (1950). Inspect for quality assurance, Industr. Qual. Contr., 7 (1 ). Dodge, H. F. and Romig, H. G. (1929). A method of sampling inspection. Bell Syst. Tech. 140
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Multiattribute acceptance sampling
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e) the cost functions are linear an
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Contents 0.1 Purpose of sampling in
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Part 0 : Introduction 0.1 Purpose o
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ectification is defined by setting
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procurement of materials of the US
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average defective from such a proce
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0.4 Scope of the present inquiry 0.
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0.4.6 A generalized acceptance samp
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sembled units the general practice
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with the above purpose in mind. The
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equals to p i in the long run. We a
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m j , r∏ −P C j ′ = g(x j , m
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show that if we increase c 2 keepin
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independence and mutually exclusive
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different sampling schemes in terms
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curves i.e. the OC curve as a funct
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0.5.3 Part3 Bayesian multiattribute
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K(N, n)/(A 1 − R 1 ) = nk ′ s +
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example, a sample of finished garme
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...(1.1.2) If now X i is assumed to
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(i) Poisson as approximation to bin
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1.2 Multiattribute sampling schemes
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We take this case and the case of t
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highest with respect to critical de
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...(1.2.4) Note that, for single at
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...(1.2.7) To compare the relative
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Corollary For ρ 2 /ρ 1 > c 2 /c 1
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increasing in each a i . Note that
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Figure 1.2.1 Absolute value of Slop
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Table 1.2.5: Three attribute A kind
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Table 1.2.5 (contd.): Three attribu
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Table 1.2.5 (contd.) : Three attrib
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Table 1.2.5 (contd.) : Three attrib
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1.3 Multiattribute sampling plans o
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Let M 2 < M 1 . Then G(c 1 , M 1 ρ
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α, β, and ρ. For α = 0.05, β =
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ma β (a 1 , a 2 , ρ) = m ′ ...(
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Table:1.3.2: Construction parameter
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1.3.5 D kind plans of given strengt
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Figure 1.3.1: The sketch of the fun
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2.1 General cost models 2.1.1 Scope
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(j) Interaction of scrappable attri
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use of defective item is additive o
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2.1.5 Approximation under Poisson c
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2.2 The expected cost model for dis
Page 93 and 94: P (p (j) ) denotes the probability
Page 95 and 96: 250 200 Figure 2.2.1 : Lot Quality
Page 97 and 98: Table 2. 2. 1 Testing goodness of f
Page 99 and 100: 2. 3 Cost of MASSP's of A kind 2.3.
Page 101 and 102: From (2.3.2) we observe that γ 2 1
Page 103 and 104: ( γ / ) 2 γ − ( m′− m e ) /
Page 105 and 106: We now consider the following funct
Page 107 and 108: ….(2.3.12) Here S A denotes the s
Page 109 and 110: ∴There exists an optimal A plan f
Page 111 and 112: Hence, Q(m) = 1 − P (m) has the s
Page 113 and 114: where a 0 and n 0 are the parameter
Page 115 and 116: where, p = (p 1 , p 2 , ..., p r )
Page 117 and 118: situations as above. For the third
Page 119 and 120: 2.5.3 Optimal Bayesian plans in sit
Page 121 and 122: Table 2.5.2 : Optimal multi attribu
Page 123 and 124: Annexure Microsoft Visual Basic pro
Page 125 and 126: If Regret,OMREGRET(c3-1) Tjem Prevo
Page 127 and 128: 3.1 Bayesian single sampling multia
Page 129 and 130: f(p i , ¯p i , s i )dp i = e −v
Page 131 and 132: easonable to assume that the p i
Page 133 and 134: the minimum cost is obtained at a 1
Page 135 and 136: Table 3.1.1 (Contd.) : Results of 1
Page 137 and 138: Table 3.1.2 : Testing goodness of f
Page 139 and 140: Figure 3.1.1 : Scatter plots of num
Page 141 and 142: Figure 3.1.2: Sketch of the cost fu
Page 143: Annexure Microsoft Visual Basic pro
Page 147 and 148: Copenhagen. Hamaker, H. C. (1950).
Page 149: Taylor, E. F. (1957). Discovery sam
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